Ask someone about their IoT data strategy and be prepared to hear about the challenges of collecting, transmitting and normalizing device data. It is not surprising due to the explosive growth of machine generated data, which represents a new frontier for data management and analysis. However, device data alone will not help you achieve your IoT objectives.
To me, IoT is an extension of Big Data initiatives that companies have already embraced as a way of gaining visibility into business operations with the goal of deriving business insights. Unfortunately, many businesses are treating these as two separate worlds: enterprise data and device data. To realize the dream of IoT, you need a strategy that bridges this gap.
Let’s also not forget about publically available data sets as well. For example, think about a remote diagnostics use case of a critical piece of construction equipment at a job site. You will want to reason over real-time operating parameters on the equipment, back-office systems containing maintenance records, and current weather conditions for a complete picture of how it operates.
The message here is that you cannot look at device data in a vacuum. You will get the best results by combining device data, enterprise data, and public data to achieve your IoT goals and create better business outcomes.